Search results

Nucleic acid testing is an effective method of accurate prevention and control and a key measure to block the spread of the epidemic. However, the fraud in nucleic acid testing occurred frequently during epidemics. This paper aims to provide a viable scheme for the government to strengthen the supervision of nucleic acid testing and to provide a new condition for the punishment for the negative act of the government and the upper limit of the reward for nucleic acid testing institution of no data fraud.

This paper formulates an evolutionary game model between the government and nucleic acid testing institution under four different mechanisms of reward and punishment to solve the issue of nucleic acid testing supervision. The authors discuss the stability of equilibrium points under the four distinct strategies and conduct simulation experiments.

The authors find that the strategy of dynamic reward and static penalty outperforms the strategies of static reward and static penalty, dynamic reward and static penalty, static reward and dynamic penalty, dynamic reward and dynamic penalty. The results reveal the appropriate punishment for the negative act of the government can enhance the positivity of the government's supervision in the strategy of dynamic reward and static penalty, while the upper limit of the reward for nucleic acid testing institution of no data fraud cannot be too high. Otherwise, it will backfire. Another interesting and counterintuitive result is that in the strategy of dynamic reward and dynamic penalty, the upper limit of the penalty for data fraud of nucleic acid testing institution cannot be augmented recklessly. Otherwise, it will diminish the government's positivity for supervision.

Most of the existing evolutionary game researches related to the reward and punishment mechanism and data fraud merely highlight that increasing the intensity of reward and punishment can help improve the government's supervision initiative and can minimize data fraud of nucleic acid institution, but they fall short of the boundary conditions for the punishment and reward mechanism. Previous literature only study the supervision of nucleic acid testing qualitatively and lacks quantitative research. Moreover, they do not depict the problem scenario of testing data fraud of nucleic acid institution regulated by the government via the evolutionary game model. Thus, this study effectively bridges these gaps. This research is universal and can be extended to other industries.

Few of the established risk identification methods refer to low-severity yet high-frequency safety risks data that may lead to several safety risks being ignored, thus reducing the potential of learning from a considerable number of cases. The purpose of this study is to explore a new valid method based on preaccident safety supervision data to identify these minor construction safety risks during routine construction operations.

A total of 329 official construction safety supervision reports containing 5,159 safety problem records from Shanghai between 2016 and 2018 served as raw material for in-depth analysis. Given the characteristics of the data collected, text mining integrated with natural language processing was applied to review the supervision reports and group safety risks automatically.

This study clarifies the way in which the supervision data should be employed to analyze high-frequency–low-severity safety risks. From these data, seven unsafe-act-related and nine unsafe-condition-related risks are identified. Regarding unsafe-act-related risks, inappropriate human behaviors could usually occur in personnel management, contract management, expense management, material management and acceptance work. For unsafe-condition-related risks, hoisting, scaffolding and reinforcement works are the main generators of onsite safety hazards during construction operations.

The study includes implications for project managers and supervisors to facilitate more effective proactive risk management by paying more attention to collecting and employing the supervision data established in each routine inspection.

Whereas previous research focused on analyzing severe accidents, this study seeks to identify the high-frequency–low-severity construction safety risks using the preaccident supervision data. The findings could provide a new thought and research direction for construction safety risk management.

Supervision is considered the signature pedagogy and after direct client practice it is rated as the number one learning and development method by practitioners. However, as we set out in this chapter, the relationship between supervision and client outcomes is not a strong one. Drawing on the use of routine outcome data, we demonstrate how clinical supervision can be improved when both practitioner and supervisor demonstrate an attitude of humility and a willingness to learn from the data they collect. Using these data to enhance supervision by improving client outcomes and to inform deliberate practice (DP) is the hall mark of the effective twenty-first century practitioner and supervisor.

The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It is a big challenge for the government to carry out financial market risk management in the big data era.

In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value.

Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike information criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored.

Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy.

The study adopts the conservation of resources (COR) theory for providing a better theoretical understanding of punitive supervision as an antecedent of employees’ minor deviant behaviors (namely, employee time theft and knowledge hiding) via creating cognitive mechanisms (employees’ perceived incivility). The purpose of this paper is to examine the moderating role of employees’ RESILIENCY on employees’ ability to buffer the impacts of punitive supervision.

Data was gathered from 265 frontline hospitality employees in Pakistan. A survey was administered in person to establish trust and rapport with employees and so, collect reliable data.

The findings confirmed a direct and mediated impact of punitive supervision on employee minor deviant behaviors via creating perceived incivility. The moderating role of employees’ resiliency was also confirmed, as the employees’ resiliency helped them mitigate the impact of punitive supervision on perceived incivility.

Data was collected from employees’ perceptions working in one industry and cultural setting. As employees’ perceptions (influenced by their cultural background) significantly affect their interpretations and reactions to punitive behavior, future research should validate and refine the findings by collecting data from a wider and diversified cultural and industry setting.

The findings provide theoretical explanatory power of the drivers and the contextual factors leading to minor employee deviant behaviors. The findings guide managers on how to develop pro-active and re-active strategies for deterring the occurrence and eliminating the consequences of punitive supervision.

This study contributes to the literature in multiple ways. It identifies and validates punitive supervision as an antecedent of Deviant Work Behavior (DWB). It provides a theoretical underpinning for explaining how punitive supervision spurs cognitive mechanisms, which in turn drive DWB. It also studies the nexus between destructive supervision and its outcomes in its entirety by studying the mediated and the moderating impacts of punitive supervision and perceived incivility, respectively.

A growing number of foreign consulting firms have been engaged in the Chinese construction market where the supervision system, as a kind of engineering consultant service has been widely implemented. However, the performance of supervision system varies significantly across regions. Therefore, foreign consulting firms are in desperate need of related performance benchmarking statistics to determine the niche market. A major issue is lack of a quantitative method to objectively evaluate regional supervision systems’ performance. The paper aims to discuss these issues.

A new regional construction supervision system benchmarking model was developed via the principal component analysis method. This model is based on key performance indicators drawn from annual official statistics. This list of key indicators was refined by means of a focus group with selected experts. Consequently the performances of all 31 regional supervision systems were calculated and ranked.

Results indicate a general gap between coastal and inland regions. The various development trends of top 10 regions are analyzed and the underlying reasons are explored. Furthermore, the regions deserving more attention or currently lacking in development are identified. The supervision industry in China is shifting from a labor-intensive industry to a knowledge-intensive industry.

The proposed model provides a single quantitative parameter to conveniently benchmark the performance of various regions. The findings are vital for the benchmarking and clarification of future endeavor of the regional supervision systems and for foreign enterprises that are planning to enter the real-estate market in China.

The purpose of this paper is to analyze the factors influencing the behaviors of government officials during the implementation of open government data (OGD). By identifying and understanding the key factors that determine government officials' adoption of OGD in China, this study can create a valuable reference for other countries and their decision-making regarding government implementation of OGD.

This research collected data by in-depth interviews with government officials in Chinese OGD departments. Through these interviews, the authors consulted 15 administrators from departments that are responsible for the information tasks in Beijing and other cities on their opinions about OGD. The authors also interviewed senior executives from information technology (IT) companies, as well as open data policy scholars from big data alliance and research institutions.

This paper provides insights about how to promote government officials in OGD implementation, including (1) strengthen social supervision for the environment, through developing and publishing OGD technology roadmaps, then attracting the public to actively participate in the implementing of OGD; (2) establish an OGD assessment mechanism for government officials, with bonus motivations, position promotion incentives, as well as spiritual incentives via regional or sector rankings; (3) alleviate the risks of officials' OGD decisions in actual practice, using the institution construction of OGD to guide its direction and strengthen security protection.

This paper fulfils an identified need to study how government officials' behavior can be motivated on OGD implementation.

Data has become a factor of production. This occurs when history enters the era of big data, in which technologies such as artificial intelligence, cloud computing and blockchain are used to collect, manipulate, mine and process data. Data is a special product of labor, a sub-derivative of other production factors.

The data factor has a dual attribute: being physical (technical) and social. The social attribute of the data factor can not only materialize the technical attribute but also amplify it. In other words, the data has a multiplication effect on the allocation efficiency of other production factors. The social attribute of the data is brought out via the technical attribute as the medium. From a technical perspective, this medium is strongly adhesive, and after being bonded with other factors of production, it will only lead to a physical reaction and not change the nature of other factors.

However, once these two attributes interact with each other, especially when data is combined with capital, the most adhesive factor in the market economy, a series of new social relations will then be produced based on the technical attribute, resulting in significant adjustments in social relations, involving both positive and negative externalities.

Therefore, to get a scientific understanding of the dual attribute and its interaction effects on the data factor, it is necessary to take the following steps. We should promote institutional design that amplifies the positive externality, with a focus on facilitating public data sharing and improving the value of commercial data development. Also, we need to strengthen institutional arrangements that prevent and control the negative externality by emphasizing data supervision based on data types and levels as well as the rule of law.

This study aims to propose a model based on philosophical thoughts of Dr S.R Ranganathan and the lean-startup method for the execution of big data analytics (BDA) in libraries. The research paves a way to understand the role and required competencies of Library and Information Science (LIS) professionals for the implementation of BDA in libraries.

In the BDA analytics context, a session with a proposed model was presented to the audience to get the response of librarians about the required competencies and skills. The research tool was developed based on the literature review to know the role of LIS professionals and their required competencies/skills for BDA. The questionnaire was distributed in the BDA session to collect the responses of the participating audience on the variables that focused on the role and core competencies of LIS professionals in BDA. In the analysis of results, the independent t-test was applied to know the mean value of the overall response rate.

The findings show that perceptions of LIS professionals in the understanding of BDA ranked high in data privacy, data availability, data organization and data literacy. Digital data curation, policies supervision and providing the data consultancy also showed a significant relationship among these variables. Besides, the correlation between the required skills for BDA, metadata skills, data ethics, data acquisition, data cleaning, data organization, data analysis, digital curation, data clustering, data protection rules and digital visualization also showed a beneficial relationship.

This study also helps to understand the perspective of LIS professionals for the implementation of BDA in libraries and to fill the literature gap in the respective.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.